8,618 research outputs found

    MARKETING OF COTTON FIBER IN THE PRESENCE OF YIELD AND PRICE RISK

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    An expected utility model and a chance constrained linear programming model were used to analyze four marketing strategies and seven crop insurance alternatives in cotton marketing in Georgia. The results obtained suggest that the existing marketing tools and insurance alternatives can be used successfully as a substitute for government support.Demand and Price Analysis, Marketing, Risk and Uncertainty,

    Theory and Applications of Robust Optimization

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    In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.Comment: 50 page

    A NEW PERSPECTIVE ON UNDERINVESTMENT IN AGRICULTURAL R&D

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    During the past 40 years, the returns to agricultural R&D have been on average in the range of 40-60% (Alston, et al 2000, Evenson 2001). Many agricultural economists see this high average as convincing evidence that there is significant underinvestment in public agricultural R&D (Ruttan 1980, Pinstrup-Andersen 2001). This paper sheds new light on the underinvestment hypothesis by introducing a simple model of the selection of R&D projects and confronting it with the rate-of-return evidence accumulated over the years worldwide. The model assumes that the distribution of all possible R&D projects on an expected rate-of-return (ERR) scale declines asymptotically. Under the neoclassical conditions of full information and profit maximization, R&D project selection starts with the project with the highest ERR and continues until the budget is finished or the last project hits the social cutoff rate, whichever comes first. Hence the underinvestment gap can be defined as the difference between the ERR of the marginal R&D project (the actual cutoff rate) and the social cutoff rate. Only three variables need to be known to estimate the underinvestment gap: the social cutoff rate, the actual cutoff rate, and the slope coefficient. Taking less than full information and economic rationality into account, the paper discusses how the latter two can be derived from a sufficiently large and representative sample of ex-post rates of return on agricultural R&D. Important findings of the model are: · Not the mean but the mode of the ex-post rate-of-return distribution is the relevant variable for assessing underinvestment in agricultural R&D. · Under the assumption of full information and profit maximization, developed countries could have invested about 40% more in public agricultural R&D and developing countries about 137% more. In terms of agricultural R&D intensity (i.e., R&D expenditures as a percentage of AgGDP), developed countries could have invested 2.8% rather than 2.0%, and developing countries 1.0% rather than 0.4% in 1981-85. · Low investment in public agricultural R&D in developing countries is caused foremost by a relatively smaller portfolio of profitable R&D projects to choose from. Underinvestment certainly plays a role (the gap is bigger for developing countries), but it explains only a small part of the difference in agricultural R&D intensity between developed and developing countries. · While efforts to reduce the underinvestment gap should continue (e.g., better priority setting and mobilization of political support), more emphasis should be placed on designing policies that help to shift (the portfolio of) R&D projects higher up on the ERR scale, even at the risk of increasing the underinvestment gap. Key words: agricultural R&D, underinvestment, rate of return, research intensitiesagricultural R&D, underinvestment, rate of return, research intensities, Research and Development/Tech Change/Emerging Technologies,

    Slow Adaptive OFDMA Systems Through Chance Constrained Programming

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    Adaptive OFDMA has recently been recognized as a promising technique for providing high spectral efficiency in future broadband wireless systems. The research over the last decade on adaptive OFDMA systems has focused on adapting the allocation of radio resources, such as subcarriers and power, to the instantaneous channel conditions of all users. However, such "fast" adaptation requires high computational complexity and excessive signaling overhead. This hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes a slow adaptive OFDMA scheme, in which the subcarrier allocation is updated on a much slower timescale than that of the fluctuation of instantaneous channel conditions. Meanwhile, the data rate requirements of individual users are accommodated on the fast timescale with high probability, thereby meeting the requirements except occasional outage. Such an objective has a natural chance constrained programming formulation, which is known to be intractable. To circumvent this difficulty, we formulate safe tractable constraints for the problem based on recent advances in chance constrained programming. We then develop a polynomial-time algorithm for computing an optimal solution to the reformulated problem. Our results show that the proposed slow adaptation scheme drastically reduces both computational cost and control signaling overhead when compared with the conventional fast adaptive OFDMA. Our work can be viewed as an initial attempt to apply the chance constrained programming methodology to wireless system designs. Given that most wireless systems can tolerate an occasional dip in the quality of service, we hope that the proposed methodology will find further applications in wireless communications

    The application of water cycle algorithm to portfolio selection

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    Portfolio selection is one of the most vital financial problems in literature. The studied problem is a nonlinear multi-objective problem which has been solved by a variety of heuristic and metaheuristic techniques. In this article, a metaheuristic optimiser, the multiobjective water cycle algorithm (MOWCA), is represented to find efficient frontiers associated with the standard mean-variance (MV) portfolio optimisation model. The inspired concept of WCA is based on the simulation of water cycle process in the nature. Computational results are obtained for analyses of daily data for the period January 2012 to December 2014, including S&P100 in the US, Hang Seng in Hong Kong, FTSE100 in the UK, and DAX100 in Germany. The performance of the MOWCA for solving portfolio optimisation problems has been evaluated in comparison with other multi-objective optimisers including the NSGA-II and multiobjective particle swarm optimisation (MOPSO). Four well-known performance metrics are used to compare the reported optimisers. Statistical optimisation results indicate that the applied MOWCA is an efficient and practical optimiser compared with the other methods for handling portfolio optimisation problems

    Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory

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    [EN] The present research proposes a novel methodology to solve the problems faced by investors who take into consideration different investment criteria in a fuzzy context. The approach extends the stochastic mean-variance model to a fuzzy multiobjective model where liquidity is considered to quantify portfolio's performance, apart from the usual metrics like return and risk. The uncertainty of the future returns and the future liquidity of the potential assets are modelled employing trapezoidal fuzzy numbers. The decision process of the proposed approach considers that portfolio selection is a multidimensional issue and also some realistic constraints applied by investors. Particularly, this approach optimizes the expected return, the risk and the expected liquidity of the portfolio, considering bound constraints and cardinality restrictions. As a result, an optimization problem for the constraint portfolio appears, which is solved by means of the NSGA-II algorithm. This study defines the credibilistic Sortino ratio and the credibilistic STARR ratio for selecting the optimal portfolio. An empirical study on the S&P100 index is included to show the performance of the model in practical applications. The results obtained demonstrate that the novel approach can beat the index in terms of return and risk in the analyzed period, from 2008 until 2018.García García, F.; González-Bueno, J.; Guijarro, F.; Oliver-Muncharaz, J.; Tamosiuniene, R. (2020). Multiobjective Approach to Portfolio Optimization in the Light of the Credibility Theory. Technological and Economic Development of Economy (Online). 26(6):1165-1186. https://doi.org/10.3846/tede.2020.13189S11651186266Acerbi, C., & Tasche, D. (2002). On the coherence of expected shortfall. Journal of Banking & Finance, 26(7), 1487-1503. doi:10.1016/s0378-4266(02)00283-2Ahmed, A., Ali, R., Ejaz, A., & Ahmad, I. (2018). 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